Deep Learning-Based Surrogate-Assisted Intelligent Optimization Framework for Reservoir Production Schemes

Determination of reservoir production schemes has always been a difficult problem during the close-loop management of waterflooding reservoir. Different well control results in significant influence on production, water breakthrough time and recovery rate of producing wells, especially in heterogeneous reservoirs. To optimize well controls, a new method using transpose convolution neural network (TCNN) surrogate model and adaptive differential evolution with optional external archive (JADE) algorithm was introduced. In this method, the TCNN surrogate model, which uses image processing, took well controls (i.e., bottom hole pressure and injection rate) and production time as parameters to predict oil saturation and pressure distribution fields at different time periods. It could well replace a numerical simulator, accurately predict the regional production dynamics at different production time steps, and significantly reduce the simulation time during the optimization process. Meanwhile, the JADE algorithm, as an improved differential evolution algorithm, greatly improved the convergence rate while ensuring the search breadth and it was suitable for solving multi-parameter well control optimization problems. Using a comprehensive reservoir optimization problem as an example, the selection and setting of some parameters during the TCNN training and JADE optimization are discussed. Finally, the method was applied to a real 3D reservoir. The computational speed of the TCNN model was about 3600 times and 2300 times faster than that of a numerical simulation model for the synthetic reservoir and L43 block, respectively.